Role of V protein RNA binding in inhibition of measles virus minigenome replication

Measles virus V protein represses genome replication through a poorly understood mechanism, which led us to investigate whether V protein might be an RNA-binding modulatory factor. Recombinant V protein, expressed from transfected HEp-2 cells or E. coli, formed protein-RNA complexes with poly-guanosine (poly-G) or poly-U linked to agarose beads. RNA binding was not exclusive to ribonucleotide homopolymers as complex formation between V protein and an RNA molecule equivalent to the 3' terminal 107 bases of the measles virus genome was observed with an electrophoretic mobility shift assay (EMSA). The interaction with poly-G was used to further examine the RNA binding properties of V demonstrating that protein-RNA complex formation was dependent upon the unique Cys-rich carboxy terminus, a region also required to induce maximal repression of minireplicon-encoded reporter gene expression in transient assays. Surprisingly, two mutant proteins that contained Cys-to-Ala substitutions in the C-terminus were found to retain their ability to bind poly-G binding and repress minireplicon reporter gene expression indicating that neither activity was dependent on the integrity of all 7 C-terminal Cys residues. Additional genetic analysis revealed that amino acids 238-266 were necessary for efficient RNA binding and overlapped with residues (238-278) required for maximal repression induced by the C-terminal domain. In addition, a 10 amino acid deletion was identified (residues 238-247) that blocked RNA binding and repression indicating that these two activities were related.     

Finding friends and enemies in an enemies-only network: a graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions

The yeast synthetic lethal genetic interaction network contains rich information about underlying pathways and protein complexes as well as new genetic interactions yet to be discovered. We have developed a graph diffusion kernel as a unified framework for inferring complex/pathway membership analogous to "friends" and genetic interactions analogous to "enemies" from the genetic interaction network. When applied to the Saccharomyces cerevisiae synthetic lethal genetic interaction network, we can achieve a precision around 50% with 20% to 50% recall in the genome-wide prediction of new genetic interactions, supported by experimental validation. The kernels show significant improvement over previous best methods for predicting genetic interactions and protein co-complex membership from genetic interaction data.     

Graemlin: general and robust alignment of multiple large interaction networks

The recent proliferation of protein interaction networks has motivated research into network alignment: the cross-species comparison of conserved functional modules. Previous studies have laid the foundations for such comparisons and demonstrated their power on a select set of sparse interaction networks. Recently, however, new computational techniques have produced hundreds of predicted interaction networks with interconnection densities that push existing alignment algorithms to their limits. To find conserved functional modules in these new networks, we have developed Graemlin, the first algorithm capable of scalable multiple network alignment. Graemlin's explicit model of functional evolution allows both the generalization of existing alignment scoring schemes and the location of conserved network topologies other than protein complexes and metabolic pathways. To assess Graemlin's performance, we have developed the first quantitative benchmarks for network alignment, which allow comparisons of algorithms in terms of their ability to recapitulate the KEGG database of conserved functional modules. We find that Graemlin achieves substantial scalability gains over previous methods while improving sensitivity.     

基于支持向量回归方法的蛋白残基可溶性预测

介绍了一种从蛋白质序列预测残基相对可溶性的新方法。该方法基于支持向量回归，并将序列局部信息作为输入。不同于先前的大部分预测方法仅对特定的蛋白残基相对可溶性进行状态分类，该方法预测了相对可溶性的连续值，从而比状态分类保留了蛋白质三维结构的更多信息。本研究对RS-126，Manesh-215和CB-513三个数据集进行了测试。通过比较不同的参数及窗宽模型来获得最佳结果，采用平均绝对误差、相关系数等参数来衡量预测效果，同时与多层反馈神经网络方法（RVP-Net）的实验结果比较，在3-fold情况下三个数据集预测结果的平均绝对误差均有降低，相关系数均有提高。另外，该算法采用了多序列比对作为输入，效果比单序列有所提高。采用该方法，对CB-513数据集平均绝对误差可以达到16．8％、相关系数为0．562，而用RVP-Net方法分别为18．8％和0．480。这些结论表明支持向量回归方法是蛋白质序列分析的一种有效工具。     

The structural basis for intramembrane assembly of an activating immunoreceptor complex

Many receptors that activate cells of the immune system are multisubunit membrane protein complexes in which ligand recognition and signaling functions are contributed by separate protein modules. Receptors and signaling subunits assemble through contacts among basic and acidic residues in their transmembrane domains to form the functional complexes. Here we report the nuclear magnetic resonance (NMR) structure of the membrane-embedded, heterotrimeric assembly formed by association of the DAP12 signaling module with the natural killer (NK) cell-activating receptor NKG2C. The main intramembrane contact site is formed by a complex electrostatic network involving five hydrophilic transmembrane residues. Functional mutagenesis demonstrated that similar polar intramembrane motifs are also important for assembly of the NK cell-activating NKG2D-DAP10 complex and the T cell antigen receptor (TCR)-invariant signaling protein CD3 complex. This structural motif therefore lies at the core of the molecular organization of many activating immunoreceptors.     

Limited proteolysis of alfalfa mosaic virus: influence on the structural and biological function of the coat protein

Limited tryptic digestion of intact alfalfa mosaic virus resulted in the quantitative removal of 27 amino acids from the N-terminal portions of the coat protein subunits. The release of this peptide material, which contains a relatively high number of basic residues, causes a breakdown of the bacilliform viral components into spherical nucleoprotein particles. From this it was concluded that the proteolytic cleavage interferes with protein-RNA interactions in the virus, but not with protein-protein interactions. The release of the N-terminal peptide also destroyed the capacity of the coat protein to activate the alfalfa mosaic virus genome. This supports the hypothesis that this activation is accomplished by a specific interaction of the coat protein with alfalfa mosaic virus RNAs.     

基于氨基酸疏水性与PSSM构建ANN预测蛋白质二级结构

目的 预测蛋白质二级结构是预测其空间结构的基础,提高蛋白质二级结构的预测率非常重要.方法在本研究中,结合氨基酸的疏水性与含有进化信息的位置特异性得分矩阵(PSSM),构建BP神经网络.本文的数据来源于蛋白质数据集合CB513,在此集合中去除氨基酸个数小于30及含有X、B的序列,共492条蛋白序列作为数据集.通过4-交互验证预测准确率.在本研究中,将蛋白质二级结构预测的结果与仅用PSSM作为输入的神经网络预测相比较.结果 采用疏水性与进化信息相结合作为输入所构建的神经网络对α螺旋的预测准确率有了较大的提高,达到近79%,敏感性及特异性分别达到79%及91%.同时对二级结构总体预测准确率达到75.96%.结论 此种方法构建的BP网络能提高蛋白质二级结构,尤其是α螺旋的预测准确率.     

Prioritization of potential candidate disease genes by topological similarity of protein–protein interaction network and phenotype data

Identifying candidate disease genes is important to improve medical care. However, this task is challenging in the post-genomic era. Several computational approaches have been proposed to prioritize potential candidate genes relying on protein–protein interaction (PPI) networks. However, the experimental PPI network is usually liable to contain a number of spurious interactions. In this paper, we construct a reliable heterogeneous network by fusing multiple networks, a PPI network reconstructed by topological similarity, a phenotype similarity network and known associations between diseases and genes. We then devise a random walk-based algorithm on the reliable heterogeneous network called RWRHN to prioritize potential candidate genes for inherited diseases. The results of leave-one-out cross-validation experiments show that the RWRHN algorithm has better performance than the RWRH and CIPHER methods in inferring disease genes. Furthermore, RWRHN is used to predict novel causal genes for 16 diseases, including breast cancer, diabetes mellitus type 2, and prostate cancer, as well as to detect disease-related protein complexes. The top predictions are supported by literature evidence.     

Prioritization of candidate disease genes by combining topological similarity and semantic similarity

The identification of gene–phenotype relationships is very important for the treatment of human diseases. Studies have shown that genes causing the same or similar phenotypes tend to interact with each other in a protein–protein interaction (PPI) network. Thus, many identification methods based on the PPI network model have achieved good results. However, in the PPI network, some interactions between the proteins encoded by candidate gene and the proteins encoded by known disease genes are very weak. Therefore, some studies have combined the PPI network with other genomic information and reported good predictive performances. However, we believe that the results could be further improved. In this paper, we propose a new method that uses the semantic similarity between the candidate gene and known disease genes to set the initial probability vector of a random walk with a restart algorithm in a human PPI network. The effectiveness of our method was demonstrated by leave-one-out cross-validation, and the experimental results indicated that our method outperformed other methods. Additionally, our method can predict new causative genes of multifactor diseases, including Parkinson’s disease, breast cancer and obesity. The top predictions were good and consistent with the findings in the literature, which further illustrates the effectiveness of our method.     

A highly basic cyanogen bromide peptide from sowbane mosaic virus protein

The protein of sowbane mosaic virus was cleaved with cyanogen bromide (CNBr) and a highly basic peptide, sCB-1, was isolated by Sephadex and ion-exchange chromatography. The amino acid composition and sodium dodecyl sulfate-polyacrylamide gel electrophoresis indicated that sCB-1 had 49 amino acid residues. Eighteen of these residues are basic, fifteen are lysine, and three are arginine. sCB-1 moved faster in polyacrylamide gel electrophoresis at pH 2.2 than a similar sized CNBr peptide from the bean strain of southern bean mosaic virus with twelve basic residues. The highly basic nature of sCB-1 suggests that this portion of the SoMV protein sequence is involved in protein-RNA binding in the virus particles.     

Special organization of the HLA-G protein on the cell surface

The human leukocyte antigen G (HLA-G) molecule possesses unique properties such as low polymorphism and restricted distribution mainly to the extravillous cytotrophoblast (EVT) cells. The EVT cells vigorously penetrate into the maternal decidual tissues and are found in contact with maternal lymphocytes, mainly with natural killer (NK) cells. The HLA-G molecule inhibits the effector function of maternal NK cells via interaction with the KIR2DL4 and the ILT-2 inhibitory NK receptors. Previously, we have demonstrated that complexes of the HLA-G protein are expressed on the cell surface. We reported that these complexes are formed due to the presence of two unique cysteine residues located at positions 42 and 147. Finally, we demonstrated that efficient binding and function of ILT-2 is dependent on the presence of HLA-G complexes on the cell surface. Here we expand the significance of these observations by revealing that complexes of HLA-G are present on the cell surface using different assays and cell lines and further demonstrate that complexes of HLA-G might be present in a soluble form after interaction with ILT-2. Therefore, the HLA-G molecule has developed a special mechanism to increase the avidity of NK receptors to the HLA-G molecule, which provides better protection for the fetus from maternal NK rejection.     

A multi-layered approach to protein data integration for diabetes research

OBJECTIVE: Recent advances in high-throughput experimental techniques have enabled many protein-protein interactions to be identified and stored in large databases. Understanding protein interactions is fundamental to the advancement of science and medical knowledge, unfortunately the scale of the requires an automated approach to analysis. We describe our graph-mining techniques to identify important structures within protein-protein interaction networks to aid in human comprehension and computerised analysis. METHODS AND MATERIALS: We describe our techniques for characterizing graph type and associated properties which is constructed from data collated from the Human Protein Reference Database. Using random graph rewiring comparative techniques and cross-validation with other identification methods a further analysis of the identified essential proteins is presented to illustrate the accuracy of these measures. We argue for using techniques based upon graph structure for separating and encapsulating proteins based upon functionality. RESULTS: We demonstrate how rational Erdos numbers may be used as a method to identify collaborating proteins based solely upon network structure. Further, by using dynamic cut-off limit it demonstrates how collaboration subgraphs can be generated for each protein within the network, and how graph containment can be used as a means of identifying which of many possible graphs are likely to be actual protein complexes. The demonstration protein interaction network built for diabetes is found to be a scale-free, small-world graph with a power-law degree distribution of interactions on nodes. These findings are consistent with many other protein interaction networks.     

FAMS complex: a fully automated homology modeling system for protein complex structures

The formation of a protein-protein complex is responsible for many biological functions; therefore, three-dimensional structures of protein complexes are essential for deeper understandings of protein functions and the mechanisms of diseases at the atomic level. However, compared with individual proteins, complex structures are difficult to solve experimentally because of technical limitations. Thus a method that can predict protein complex structures would be invaluable. In this study, we developed new software, FAMS Complex; a fully automated homology modeling system for protein complex structures consisting of two or more molecules. FAMS Complex requires only sequences and alignments of the target protein as input and constructs all molecules simultaneously and automatically. FAMS Complex is likely to become an essential tool for structure-based drug design, such as in silico screening to accelerate drug discovery before an experimental structure is solved. Moreover, in this post-genomic era when huge amounts of protein sequence information are available, a major goal is the determination of protein-protein interaction networks on a genomic scale. FAMS Complex will contribute to this goal, because its procedure is fully automated and so is suited for large-scale genome wide modeling.     

Examining protein protein interactions using endogenously tagged yeast arrays: the cross-and-capture system

Comprehensive approaches to detect protein-protein interactions (PPIs) have been most successful in the yeast model system. Here we present "Cross-and-Capture," a novel assay for rapid, sensitive assessment of PPIs via pulldown of differently tagged yeast strain arrays. About 500 yeast genes that function in DNA replication, repair, and recombination and nuclear proteins of unknown function were chromosomally tagged with six histidine residues or triple VSV epitopes. We demonstrate that the assay can interrogate a wide range of previously known protein complexes with increased resolution and sensitivity. Furthermore, we use "Cross-and-Capture" to identify two novel protein complexes: Rtt101p-Mms1p and Sae2p-Mre11p. The Rtt101p-Mms1p interaction was subsequently characterized by genetic and functional analyses. Our studies establish the "Cross-and-Capture" assay as a novel, versatile tool that provides a valuable complement for the next generation of yeast proteomic studies.     

Elucidating Common Structural Features of Human Pathogenic Variations Using Large‐Scale Atomic‐Resolution Protein Networks

With the rapid growth of structural genomics, numerous protein crystal structures have become available. However, the parallel increase in knowledge of the functional principles underlying biological processes, and more specifically the underlying molecular mechanisms of disease, has been less dramatic. This notwithstanding, the study of complex cellular networks has made possible the inference of protein functions on a large scale. Here, we combine the scale of network systems biology with the resolution of traditional structural biology to generate a large‐scale atomic‐resolution interactome‐network comprising 3,398 interactions between 2,890 proteins with a well‐defined interaction interface and interface residues for each interaction. Within the framework of this atomic‐resolution network, we have explored the structural principles underlying variations causing human‐inherited disease. We find that in‐frame pathogenic variations are enriched at both the interface and in the interacting domain, suggesting that variations not only at interface “hot‐spots,” but in the entire interacting domain can result in alterations of interactions. Further, the sites of pathogenic variations are closely related to the biophysical strength of the interactions they perturb. Finally, we show that biochemical alterations consequent to these variations are considerably more disruptive than evolutionary changes, with the most significant alterations at the protein interaction interface.     

HPIminer: A text mining system for building and visualizing human protein interaction networks and pathways

The knowledge on protein–protein interactions (PPI) and their related pathways are equally important to understand the biological functions of the living cell. Such information on human proteins is highly desirable to understand the mechanism of several diseases such as cancer, diabetes, and Alzheimer’s disease. Because much of that information is buried in biomedical literature, an automated text mining system for visualizing human PPI and pathways is highly desirable. In this paper, we present HPIminer, a text mining system for visualizing human protein interactions and pathways from biomedical literature. HPIminer extracts human PPI information and PPI pairs from biomedical literature, and visualize their associated interactions, networks and pathways using two curated databases HPRD and KEGG. To our knowledge, HPIminer is the first system to build interaction networks from literature as well as curated databases. Further, the new interactions mined only from literature and not reported earlier in databases are highlighted as new. A comparative study with other similar tools shows that the resultant network is more informative and provides additional information on interacting proteins and their associated networks.     

The rotavirus nonstructural protein, NS35, possesses RNA-binding activity in vitro and in vivo.

Toward the goal of identifying and characterizing rotavirus RNA-binding proteins, we have used a gel retardation assay and protein-RNA cross-linking by ultraviolet (uv) light to examine cytoplasmic lysates prepared from SA11-infected cells for the presence of RNA-binding proteins. Analysis of band shifts produced in the gel retardation assay indicated that infected cells contained significant amounts of a viral protein which had affinity for both single-stranded and double-stranded RNA but lacked sequence specificity. Cross-linking of this protein to radiolabeled RNA in vitro followed by RNase treatment and immunoprecipitation with an anti-NS35 monoclonal antibody revealed that the RNA-binding activity was associated with NS35. Moreover, sodium dodecyl sulfate-polyacrylamide gel electrophoresis analysis of the protein-RNA complex isolated from native gels revealed that NS35 was the only viral protein component of the complex. Since NS35 expressed by translation in rabbit reticulocyte lysates exhibited affinity for poly(U)-Sepharose, NS35 must possess intrinsic RNA-binding activity that is able to function in the absence of other viral proteins. Immunoprecipitation of RNase-treated cross-links formed in intact cells following exposure to uv light confirmed that NS35 was intimately associated with ssRNA in the infected cell. On the basis of its ability to bind RNA and given that previous studies have shown that NS35 localizes to the viroplasm in infected cells, is essential for RNA replication, and is a component of replicase particles, we propose that NS35 functions to concentrate viral mRNAs in the viroplasm and that NS35-mRNA complexes serve as substrates for genome assortment and replication.     

Identification of Conserved Hydrophobic C-Terminal Residues of the Human Papillomavirus Type 1 E1E4 Protein Necessary for E4 Oligomerisationin Vivo

Previous studies have shown that human papillomavirus (HPV) E4 proteins undergo oligomerisation, although the precise sequences involved have not been identified. Using the yeast two-hybrid system we have identified HPV 1 E4 sequences that are critical to multimerisation. Fusion proteins were created by linking wild-type and mutant E4 proteins to a LexA DNA-binding domain or a B42 transactivation domain. HPV 1 E4:E4 interactions were examined by expression of these fusion proteins inSaccharomyces cerevisiae.This assay showed that (1) amino acid residues 95 to 115 at the carboxy-terminus were critical for oligomerisation and (2) hydrophobic residues (isoleucine 107, phenylalanine 114) in this domain are major determinants in the formation of oligomers. Interestingly, the carboxy-terminal domain shares homology with other E4 proteins of cutaneous HPV types and, furthermore, positions 107 and 114 are conserved residues. Substitution of the conserved aspartate amino acids (residues 110 and 112) did not abrogate E4 oligomerisation. Chemical cross-linking of wart and recombinant (baculovirus-expressed) HPV 1 E4 protein indicated that in solution this viral protein forms complexes consistent in size with either trimers or tetramers. These complexes were resistant to urea denaturation and are not dependent on the formation of disulphide linkages. A mutant protein containing a deletion of residues 110 to 115 was unable to form oligomers following cross-linking supporting a role for this region in mediating E4:E4 interactions. We conclude that oligomerisation of the HPV 1 E4 protein is likely to be mediated by carboxy-terminal residues and that conserved hydrophobic residues of this domain play a major role in E4 oligomerisation.